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The modifications are already in the github repo of FSDA
and they all be present inside the FSDA toolbox starting from release 8.5.26
Please, let us know if your suggestion has been taken into account.
For any other suggestion, please do not hesitate to contact us
Marco (on behalf of all the FSDA team)
I'd suggest expanding Powertra.m to implement standardization (to mean=0 and std. dev. =1) after BoxCox or Yeo-Johnson is applied.
This would make Powertra.m equivalent to the scikit-learn PowerTransformer frequently used in Python DA workflows.
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